

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class        _a		(_A							):


					'''simple docstring'''






					UpperCAmelCase__:  Any    				=			42
					UpperCAmelCase__:  str    				=			None



def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					Tuple=0.999       , lowercase_:					List[str]="cosine"       , )	-> Optional[Any]:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(lowercase_:					List[str]   ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(lowercase_:					Any   ):
			return math.exp(t * -12.0   )

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

	A__				:       int	  =						[]
	for i in range(a_   ):
		A__				:       int	  =						i / num_diffusion_timesteps
		A__				:       Tuple	  =						(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(a_   ) / alpha_bar_fn(a_   )       , a_   )   )
	return torch.tensor(a_       , dtype=torch.floataa   )



class        _a		(_A						,  _A							):


					'''simple docstring'''






					UpperCAmelCase__:  List[Any]    				=			1






					@register_to_config
					def __init__(					self       ,			A__ = 1000       ,			A__ = 0.0_0_0_1       ,			A__ = 0.0_2       ,			A__ = "linear"       ,			A__ = None       ,			A__ = True       ,			A__ = True       ,			A__ = 0       ,			A__ = "epsilon"       ,			A__ = 1.0       ,			**A__       ,			):
						if kwargs.get("""set_alpha_to_one"""       ,			a_     ) is not None:
							A__				:       List[str]	  =						(
							    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
							)
							deprecate("""set_alpha_to_one"""       ,			"""1.0.0"""       ,			a_       ,			standard_warn=a_     )
							A__				:       str	  =						kwargs["set_alpha_to_one"]
						if trained_betas is not None:
							A__				:       Optional[Any]	  =						torch.tensor(a_       ,			dtype=torch.floataa     )
						elif beta_schedule == "linear":
							A__				:       List[Any]	  =						torch.linspace(a_       ,			a_       ,			a_       ,			dtype=torch.floataa     )
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							A__				:       Optional[int]	  =						(
							    torch.linspace(beta_start**0.5       ,			beta_end**0.5       ,			a_       ,			dtype=torch.floataa     ) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							A__				:       Dict	  =						betas_for_alpha_bar(a_     )
						else:
							raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""     )

						A__				:       Union[str, Any]	  =						1.0 - self.betas
						A__				:       str	  =						torch.cumprod(self.alphas       ,			dim=0     )

						# At every step in inverted ddim, we are looking into the next alphas_cumprod
						# For the final step, there is no next alphas_cumprod, and the index is out of bounds
						# `set_alpha_to_zero` decides whether we set this parameter simply to zero
						# in this case, self.step() just output the predicted noise
						# or whether we use the final alpha of the "non-previous" one.
						A__				:       Union[str, Any]	  =						torch.tensor(0.0     ) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						A__				:       Any	  =						1.0

						# setable values
						A__				:       Optional[Any]	  =						None
						A__				:       Optional[Any]	  =						torch.from_numpy(np.arange(0       ,			a_     ).copy().astype(np.intaa     )     )






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						return sample






					def     __A     (					self       ,			A__       ,			A__ = None     ):

						if num_inference_steps > self.config.num_train_timesteps:
							raise ValueError(
							    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
							    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
							    F""" maximal {self.config.num_train_timesteps} timesteps."""     )

						A__				:       str	  =						num_inference_steps
						A__				:       Union[str, Any]	  =						self.config.num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						A__				:       Dict	  =						(np.arange(0       ,			a_     ) * step_ratio).round().copy().astype(np.intaa     )
						A__				:       str	  =						torch.from_numpy(a_     ).to(a_     )
						self.timesteps += self.config.steps_offset






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__ = 0.0       ,			A__ = False       ,			A__ = None       ,			A__ = True       ,			):
						# 1. get previous step value (=t+1)
						A__				:       List[str]	  =						timestep + self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						# change original implementation to exactly match noise levels for analogous forward process
						A__				:       Dict	  =						self.alphas_cumprod[timestep]
						A__				:       Optional[Any]	  =						(
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						A__				:       Dict	  =						1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						if self.config.prediction_type == "epsilon":
							A__				:       List[str]	  =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							A__				:       Optional[Any]	  =						model_output
						elif self.config.prediction_type == "sample":
							A__				:       Union[str, Any]	  =						model_output
							A__				:       Union[str, Any]	  =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
							A__				:       Optional[int]	  =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							A__				:       Any	  =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
						else:
							raise ValueError(
							    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
							    """ `v_prediction`"""     )

						# 4. Clip or threshold "predicted x_0"
						if self.config.clip_sample:
							A__				:       Tuple	  =						pred_original_sample.clamp(
							    -self.config.clip_sample_range       ,			self.config.clip_sample_range     )

						# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						A__				:       Tuple	  =						(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

						# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						A__				:       Optional[Any]	  =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if not return_dict:
							return (prev_sample, pred_original_sample)
						return DDIMSchedulerOutput(prev_sample=a_       ,			pred_original_sample=a_     )






					def __len__(					self     ):
						return self.config.num_train_timesteps










'''simple docstring'''






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "sew-d"



	def __init__( self		,   a_=32		,   a_=768		,   a_=12		,   a_=12		,   a_=3_072		,   a_=2		,   a_=512		,   a_=256		,   a_=True		,   a_=True		,   a_=("p2c", "c2p")		,   a_="layer_norm"		,   a_="gelu_python"		,   a_=0.1		,   a_=0.1		,   a_=0.1		,   a_=0.0		,   a_=0.1		,   a_=0.02		,   a_=1e-7		,   a_=1e-5		,   a_="group"		,   a_="gelu"		,   a_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,   a_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,   a_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,   a_=False		,   a_=128		,   a_=16		,   a_=True		,   a_=0.05		,   a_=10		,   a_=2		,   a_=0.0		,   a_=10		,   a_=0		,   a_="mean"		,   a_=False		,   a_=False		,   a_=256		,   a_=0		,   a_=1		,   a_=2		,   **a_		,   ):
			super().__init__(**a_		,   pad_token_id=a_		,   bos_token_id=a_		,   eos_token_id=a_						)
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= feat_extract_norm
			lowerCAmelCase  :     str       						= feat_extract_activation
			lowerCAmelCase  :     Dict       						= list(a_						)
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     Any       						= list(a_						)
			lowerCAmelCase  :     Optional[Any]       						= conv_bias
			lowerCAmelCase  :     Tuple       						= num_conv_pos_embeddings
			lowerCAmelCase  :     int       						= num_conv_pos_embedding_groups
			lowerCAmelCase  :     Optional[int]       						= len(self.conv_dim						)
			lowerCAmelCase  :     Dict       						= num_hidden_layers
			lowerCAmelCase  :     str       						= intermediate_size
			lowerCAmelCase  :     Any       						= squeeze_factor
			lowerCAmelCase  :     Optional[int]       						= max_position_embeddings
			lowerCAmelCase  :     Tuple       						= position_buckets
			lowerCAmelCase  :     Union[str, Any]       						= share_att_key
			lowerCAmelCase  :     str       						= relative_attention
			lowerCAmelCase  :     List[str]       						= norm_rel_ebd
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     Dict       						= num_attention_heads
			lowerCAmelCase  :     Optional[Any]       						= hidden_dropout
			lowerCAmelCase  :     int       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= activation_dropout
			lowerCAmelCase  :     Any       						= feat_proj_dropout
			lowerCAmelCase  :     Optional[int]       						= final_dropout
			lowerCAmelCase  :     Union[str, Any]       						= layer_norm_eps
			lowerCAmelCase  :     str       						= feature_layer_norm_eps
			lowerCAmelCase  :     Tuple       						= initializer_range
			lowerCAmelCase  :     int       						= vocab_size

			if (
			    (len(self.conv_stride						) != self.num_feat_extract_layers)
			    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
			    or (len(self.conv_dim						) != self.num_feat_extract_layers)
			):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect."
					    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
					    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
					    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

			# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
			lowerCAmelCase  :     List[str]       						= apply_spec_augment
			lowerCAmelCase  :     List[Any]       						= mask_time_prob
			lowerCAmelCase  :     Tuple       						= mask_time_length
			lowerCAmelCase  :     List[Any]       						= mask_time_min_masks
			lowerCAmelCase  :     List[str]       						= mask_feature_prob
			lowerCAmelCase  :     Union[str, Any]       						= mask_feature_length
			lowerCAmelCase  :     List[Any]       						= mask_feature_min_masks

			# ctc loss
			lowerCAmelCase  :     Any       						= ctc_loss_reduction
			lowerCAmelCase  :     str       						= ctc_zero_infinity

			# sequence classification
			lowerCAmelCase  :     Dict       						= use_weighted_layer_sum
			lowerCAmelCase  :     Tuple       						= classifier_proj_size





	@property
	def 	_lowerCamelCase     ( self						):
			return functools.reduce(operator.mul		,   self.conv_stride		,   1						)



